If you’re not a quantitative sissy, a category to which I happily consign myself, you will want to take a look at Darling’s recent paper with co-authors Fengqui You, Thomas Veselka, and Gartner analyst Alfonso Velosa. It’s bound to let the sun shine into some of the darker corners of alternative energy production.
Media reports on the global search for alternative and sustainable energy sources often dwell in the happy realms of possibility and leave me happily clinging to a cheerful bits of information they offer up––"if everyone over the age of 21 replaced on incandescent lightbulb with a fluorescent," and blah, blah––when was the last time you read such an account that came to an unhappy conclusion or the prospect of failure? And who knows whether these bits are facts or factoids, the unreliable cousins of fact. More important, where do the calculations in them come from?
I never stopped to think about the sources or pertinence of the peppy facts and factoids I like so much until I came across a brief blog mention of scientist Seth Darling at the U.S. Department of Energy’s Argonne National Laboratory. Darling is a photovoltaics expert who is trying to separate fact from factoid and frame a realistic picture of the costs of solar electrical generation. He is using his Monte Carlo software to "lift up the rug" under which many assumptions about solar energy have been swept.
Darling points out that the photovoltaics industry is expanding rapidly, with the number of its stakeholders growing in parallel: investors and funding agencies, technology developers, regulators, and policymakers. None of these stakeholders can rely on cheerful factoids. They have to make too many decisions under uncertainty, and they need reliable information on which to base statistical analysis, risk assessment, and production predictions. Darling is trying to provide an analytical framework for testing assumptions behind solar electrical production, calculating its lifetime costs, and comparing these with conventional generation methods. He calls this a "levelized cost of energy." This goes beyond immediate financial risk analysis to incorporate over the lifetime of the production resource such usually hidden variables as the cost of financing, insurance, maintenance, and depreciation.